Recurrent-Neural-Network-Based Anti-Jamming Framework for Defense Against Multiple Jamming Policies

Conventional anti-jamming methods mainly focus on preventing single jammer attacks with an invariant jamming policy or jamming attacks from multiple jammers with similar jamming policies. These anti-jamming methods are ineffective against a single jammer following several different jamming policies or multiple jammers with distinct policies. Therefore, this article proposes an anti-jamming method that can adapt its policy to the current jamming attack. Moreover, for the multiple jammers scenario, an anti-jamming method that estimates the future occupied channels using the jammers’ occupied channels in previous time slots is proposed. In both single and multiple jammers scenarios, the interaction between the users and jammers is modeled using recurrent neural networks (RNNs). The performance of the proposed anti-jamming methods is evaluated by calculating the users’ successful transmission rate (STR) and ergodic rate (ER), and compared to a baseline based on deep
$Q$
-learning (DQL). Simulation results show that for the single jammer scenario, all the considered jamming policies are perfectly detected and a high STR and ER are maintained. Moreover, when 70% of the spectrum is under jamming attacks from multiple jammers, the proposed method achieves an STR and ER greater than 75% and 80%, respectively. These values reach 90% when 30% of the spectrum is under jamming attacks. In addition, the proposed anti-jamming methods significantly outperform the DQL method for all the considered jamming scenarios.